A model reference control structure using a fuzzy neural network

Chen, Yie-Chien; Teng, Ching‐Cheng

doi:10.1016/0165-0114(94)00319-3

Cited by 243 publications

(104 citation statements)

References 9 publications

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“…Electric DC Motor converts electrical energy to mechanical energy using the electromagnetic induction principle [9]. Fig.…”

Section: Modeling a DC Motormentioning

confidence: 99%

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Anandaraju¹,

Puttaswamy²

2014

IJAREEIE

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Proportional-Integral-Derivative (PID) Controllers plays a significant role in many industrial and commercial applications. Designing of PID controller is always a challenging problem especially for high order systems. It is known that PID Controller is employed in every aspect of industrial automation. The PID Controller applications are spanned from small industry to higher technology industries. Hence, the problem is always how to optimize the PID Controller. Generally, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This simulation involves the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating range. Self-tuning method for PID Controller using Evolutionary Computational (EC) Techniques capable of providing robust design. In this paper two types of tuning methods are presented, namely Genetic Algorithm and Modified Interactive Evolutionary Computation Method. The results obtained by them based on velocity control of DC motor and harmonic analysis are analysed and a comparative study of the two are presented. KEYWORDS: DC motor, PID, Genetic Algorithm, MIEC I.INTRODUCTIONPID Controller is a generic control loop feedback mechanism and is widely used in control system. PID Controllers use three basic behavior types or modes: P -proportional, I -integral and D -derivative. While proportional and integral are together used as single control mode, a derivative mode is rarely used on its own in control systems. Combinations such as PI and PD control are very often used in practical systems. A proportional-integral-derivative controller or commonly known as PID Controller is a generic control loop feedback mechanism (controller) widely used in industrial control systems. Therefore, a PID is the most commonly used feedback controller [1]. Such type of controller estimates fault rate as the difference between a considered method variable and a required set-point. The designed controller tries to reduce the error by correcting the process control inputs. The PID Controller algorithm consists of three distinct stable parameters, and is consequently termed as three-term control, they are proportional, integral and derivative values, denoted as P, I, and D. The weighed sum of these three events is deployed to regulate the process using a control constituent. A PID Controller is the most excellent controller. The controller has a potential to facilitate the organized act programmed for specific process needed by modifying the three parameters in a PID Controller algorithm. The block diagram of a PID Controller is shown in Fig. 1

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“…Electric DC Motor converts electrical energy to mechanical energy using the electromagnetic induction principle [9]. Fig.…”

Section: Modeling a DC Motormentioning

confidence: 99%

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Anandaraju¹,

Puttaswamy²

2014

IJAREEIE

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“…As a statistical technique, fuzzy logic has gained attention as for its ability to cope with uncertainty [18,32] and imprecise linguistic concepts or fuzzy terms [33]. This is particularly true for the newly introduced type-2 fuzzy logic systems (type-2 FLS) [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

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Abstract:The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.

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“…In recent years, the number of new approaches using various neural network-based fuzzy systems has increased significantly (9)(10)(11)(12)(13)(14)(15). For example, recurrent fuzzy-neural networks control of linear induction motors (16), robot manipulator control using generalized fuzzy-neural networks (17) and, fuzzy neural networks-sliding mode control for direct adaptive control strategy (18), have been studied by researchers.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Control of a Dynamometer for the Emulation of Nonlinear Mechanical Loads

Gökbulut¹,

Dandil²

2006

JPOLTEC

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This paper proposes a neuro-fuzzy controller for a vector controlled load machine (dynamometer) which is mechanically coupled to a drive machine for the emulation of nonlinear loads. The main objective is to provide a system for testing of high performance electrical drives under nonlinear loads. The proposed emulation strategy is based on the model reference control approach using an on-line trained Neuro-Fuzzy Controller (NFC). The emulation is placed in the closed speed loop of the drive machine. Varieties of dynamic load models which are the nonlinear functions of the shaft speed are successfully emulated and the generalization capability of the trained NFC is shown. Simulation results showing the excellent performance of the proposed emulation strategy are presented.Keywords: Neuro-fuzzy controller, mechanical load emulation, permanent magnet synchronous motor (PMSM). Doğrusal Olmayan Mekanik Yüklerin Emulasyonu içinDinamometrelerin Sinirsel-Bulanık Denetimi ÖZETBu makalede, doğrusal olmayan yüklerin emulasyonunu yapmak amacıyla bir elektrik motoruna mekanik kuplaj elemanı ile bağlanan vektör denetimli yük makinesinin (dinamometre) sinirsel bulanık ağlarla denetimi gerçekleştirilmiştir. Yük emulasyonun temel amacı, elektrik sürücü sistemlerini test etmek için yük makinesini denetlemek suretiyle arzu edilen bir yük elde etmektir. Önerilen emulasyon yönteminde, gerçek zamanlı olarak eğitilen Sinirsel Bulanık Denetleyiciler (SBD) kullanarak model referans denetim yaklaşımı kullanılmıştır. Sürücü makinenin kapalı çevrim hız denetim döngüsü içerisinde yük emulasyonu gerçekleştirilmiştir. Rotor hızının doğrusal olmayan fonksiyonları ile tanımlanan çeşitli dinamik yüklerin emulasyonu yapılmış ve eğitilmiş SBD' nin farklı yük modellerini genelleme yeteneği gösterilmiştir. Simulasyon sonuçları, önerilen emulasyon yönteminin performansının son derecede iyi olduğunu göstermektedir.Anahtar Kelimeler: Sinirsel bulanık denetleyiciler (SBD), Mekanik yük emulasyonu, Kalıcı mıknatıslı senkron motorlar (KMSM).

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A model reference control structure using a fuzzy neural network

Cited by 243 publications

References 9 publications

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Neuro-Fuzzy Control of a Dynamometer for the Emulation of Nonlinear Mechanical Loads

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